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Ge XY, Funk J, Albrecht T, Birkhimer M, Gilsdorf M, Hayes M, Hu F, Maliver P, McCreary M, Nguyen T, Romero-Palomo F, Seger S, Fuji RN, Schumacher V, Sullivan R. Toxicologic Pathology Forum: A Roadmap for Building State-of-the-Art Digital Image Data Resources for Toxicologic Pathology in the Pharmaceutical Industry. Toxicol Pathol 2022; 50:942-949. [PMID: 36341579 DOI: 10.1177/01926233221132747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Digitization of histologic slides brings with it the promise of enhanced toxicologic pathology practice through the increased application of computational methods. However, the development of these advanced methods requires access to substrate image data, that is, whole slide images (WSIs). Deep learning methods, in particular, rely on extensive training data to develop robust algorithms. As a result, pharmaceutical companies interested in leveraging computational methods in their digital pathology workflows must first invest in data infrastructure to enable data access for both data scientists and pathologists. The process of building robust image data resources is challenging and includes considerations of generation, curation, and storage of WSI files, and WSI access including via linked metadata. This opinion piece describes the collective experience of building resources for WSI data in the Roche group. We elaborate on the challenges encountered and solutions developed with the goal of providing examples of how to build a data resource for digital pathology analytics in the pharmaceutical industry.
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Affiliation(s)
- Xing-Yue Ge
- Genentech Research and Early Development (gRED), Department of Development Sciences Informatics, Genentech Inc, South San Francisco, USA
| | - Juergen Funk
- Roche Pharma Research and Early Development (pRED), Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Tom Albrecht
- Roche Pharma Research and Early Development (pRED), Data & Analytics, Roche Innovation Center Basel, Switzerland
| | - Merima Birkhimer
- Genentech Research and Early Development (gRED), Department of Development Sciences Informatics, Genentech Inc, South San Francisco, USA
| | - Moritz Gilsdorf
- Roche Pharma Research and Early Development (pRED), Data & Analytics, Roche Innovation Center Basel, Switzerland
| | - Matthew Hayes
- Genentech Research and Early Development (gRED), Department of Safety Assessment, Genentech Inc, South San Francisco, USA
| | - Fangyao Hu
- Genentech Research and Early Development (gRED), Department of Safety Assessment, Genentech Inc, South San Francisco, USA
| | - Pierre Maliver
- Roche Pharma Research and Early Development (pRED), Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Mark McCreary
- Genentech Research and Early Development (gRED), Department of Development Sciences Informatics, Genentech Inc, South San Francisco, USA
| | - Trung Nguyen
- Genentech Research and Early Development (gRED), Department of Safety Assessment, Genentech Inc, South San Francisco, USA
| | - Fernando Romero-Palomo
- Roche Pharma Research and Early Development (pRED), Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Shanon Seger
- Roche Pharma Research and Early Development (pRED), Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Reina N Fuji
- Genentech Research and Early Development (gRED), Department of Safety Assessment, Genentech Inc, South San Francisco, USA
| | - Vanessa Schumacher
- Roche Pharma Research and Early Development (pRED), Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Ruth Sullivan
- Genentech Research and Early Development (gRED), Department of Safety Assessment, Genentech Inc, South San Francisco, USA
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